Inference that learns from your work.

Lower costs without compromising quality. One inference endpoint that learns from your traffic and team judgment to continuously optimize prompts, route requests, and train custom models.

Chat with us See how it works

For AI-native companies

One endpoint. Continuously optimized.

Ambral continuously tests cheaper models and prompt changes against your production workloads, then trains specialized models where the gains justify it. Every change is evaluated against your quality bar before deployment, with frontier models available for the tasks that need them.

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Point your existing client at Ambral. Requests pass through to the model you already use, and every call, tool invocation and outcome is captured as a trace.

Use built-in connectors to bring in context and outcomes from the tools you already use, giving Ambral a fuller picture of what good work looks like.

Before

client = OpenAI(
  base_url="https://api.openai.com/gpt-6.1-sol",
  api_key=OPENAI_KEY,
)

After

client = OpenAI(
  base_url="https://api.ambral.com/auto",
  api_key=AMBRAL_KEY,
)

traces · api.ambral.com/v1sample data

time	trace	agent	model	latency	eval	status
14:02:11.000	tr_9f3a1c	research-agent	ambral/research-27b	212 ms	0.94	served
14:02:14.383	tr_b07e42	code-review	claude-opus	1840 ms	0.88	captured
14:02:17.766	tr_41cd90	claims-intake	ambral/claims-27b	198 ms	0.91	eval built
14:02:20.149	tr_e2a7f5	contract-review	gpt-5	2310 ms	0.79	needs review
14:02:23.532	tr_7c1b3d	sales-outreach	ambral/sales-27b	240 ms	0.93	served

Ambral turns production traces into evals grounded in your tasks, context, and outcomes. It tests candidate models against that suite and brings uncertain cases to your team for review.

trace · tr_9f3a1c · research-agent

taskcompare three vendor filings

context12 docs · 3 tool calls

outcomeaccepted by analyst

eval #148

inputthree filings + the analyst's brief

expectedcites all three, flags the revenue gap

rubricaccuracy · citations · brevity

weightjudgment call · reviewed

suitecasesneeds review

research-agent1,24038

claims-intake86021

code-review2,11064

Connect
Traces become evals
Real traffic becomes an eval suite. No hand-written test sets, no ML team to start.
Evaluate
Your team weighs in where it matters
Ambral asks only on judgment calls. Pick one, add context, and it is remembered.
Optimize
Smaller models, same quality
Ambral optimizes prompts and, when it pays, trains smaller models on your work. Traffic switches only when evals show they match frontier quality on your workload.
Learn
Real outcomes feed back
Accepted outputs, merged changes and closed deals flow back. The model improves from how that workload went.

Have a workload in mind? Bring it to a 30-minute call.

Chat with us

Turn the work your product already does into intelligence you own, at a fraction of the cost of frontier models. Ambral optimizes across frontier and open-weight models, then trains specialized models on your tasks and feedback when the gains justify it.

data-extraction: 10%25%65%
claims-intake: 35%45%20%
code-review: 60%30%10%

Share of requests by model class, per workload. Illustrative.

Same quality. A fraction of the cost.

ambral/research-27b$0.40eval 0.95

claude-opus$6.00eval 0.94

Cost per 1k requests at matched eval score. Illustrative.

Your work deserves intelligence of its own.

Bring a workload. We’ll show you how prompt optimization, model routing, and training could improve performance and reduce inference costs.

Chat with us

Inference that learns from your work.

One endpoint. Continuously optimized.

Change one line. Keep everything else.

Your traffic writes your evals.

From traces to a model that is yours.

Traces become evals

Your team weighs in where it matters

Smaller models, same quality

Real outcomes feed back

Your intelligence. Better margins.

Your work deserves intelligence of its own.